Characterizing and Predicting TCP Throughput on the Wide Area Network

Dong Lu, Yi Qiao, Peter Dinda, Fabian Bustamante

Department of Computer ScienceNorthwestern University

http://plab.cs.northwestern.edu

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Overview

• Algorithm for predicting the TCP throughput as function of flow size

• Minimal active probing• Dynamic probe rate adjustment

• Explaining flow size / throughput correlation

• Explaining why simple active probing fails

Large scale empirical study

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Outline

• Why TCP throughput prediction?

• Particulars of study

• Flow size / TCP throughput correlation

• Issues with simple benchmarking

• DualPats algorithm

• Stability and dynamic rate adjustment

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Goal

A library call

BW = PredictTransfer(src,dst,numbytes);

Expected Time = numbytes/BW;

Ideally, we want a confidence interval:

(BWLow,BWHigh) = PredictTransfer(src,dst,numbytes,p);

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Available Bandwidth

• Maximum rate a path can offer a flow without slowing other flows– pathchar, cprobe, nettimer, delphi, IGI,

pathchirp, pathload …

• Available bandwidth can differ significantly from TCP throughput

• Not real time, takes at least tens of seconds to run

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Simple TCP Benchmarking

• Benchmark paths with a single small probe– BW = ProbeSize/Time– Widely used Network Weather Service (NWS)

and others (Remos benchmarking collector)

• Not accurate for large transfers on the current high speed Internet– Numerous papers show this and attempt to fix it

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Fixing Simple TCP Benchmarking

• Logs [Sundharshan]: correlate real transfer measurements with benchmarking measurements

• Recent transfers needed• Similar size transfers needed• Measurements at application chosen times

• CDF-matching [Swany]: correlate CDF of real transfer measurements with CDF of benchmarking measurements

• Recent transfers still needed• Measurements at application chosen times

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Analysis of TCP

• Extensive research on TCP throughput modeling in networking community

• Really intended to build better TCPs

• Difficult to use models online because of hard to measure parameters

• Future loss rate and RTT

• Note: we measure goodput

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Our Measurement Study

• PlanetLab and additional machines– Located all over the world

• Measurements of throughput– Wide open socket buffers (1-3 MB)– Simple ttcp-like client/server– scp– GridFTP

• Four separate sets of measurements

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Distribution Set

• For analysis of TCP throughput stability and distributions

• 60 randomly chosen paths among PlanetLab machines

• 1.6 million transfers (client/server)– 100 KB, 200 KB, 400 KB, … 10 MB flows– 3000 consecutive transfers per path+flow

size

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Correlation Set

• For studying correlation between throughput and flow size, initial testing of algorithm

• 60 randomly chosen paths among PlanetLab machines

• 2.4 million transfers, 270 thousand runs, client/server– 100 KB, 200 KB, 400 KB, … 10 MB flows– Run = sweep flow size for path

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Verification Set

• Test algorithm

• 30 randomly chosen paths among PlanetLab machines and others

• 4800 transfers, 300 runs, scp and GridFTP– 5 KB to 1 GB flows– Run = sweep flow size for path

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Online Evaluation Set

• Test online algorithm

• 50 randomly chosen paths among PlanetLab machines and others

• 14000 transfers, scp and GridFTP– 40 MB or 160 MB file, randomly chosen size– 10 days

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Strong Correlation Between TCP Throughput and Flow Size

Correlation andVerification Sets

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Why Does The Correlation Exist?

• Slow start and user effects [Zhang]• Extant flows

• Non-negligible startup overheads– Control messages in scp and GridFTP

• Residual slow start effect– SACK results in slow convergence to

equilibrium

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0 5000 10000 15000 20000 25000 30000 35000

File size (KB)

Tim

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Why Simple Benchmarking FailsProbes are too small

Need more than one probe to capture correlation

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File size (KB)

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Our ApproachTwo consecutive probes, both larger than the noise region

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Our Approach

• Two consecutive probes are integrated into a single probe– 400KB, 800 KB in single 800 KB probe

0 T1 T2

Probe one

Probe two

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Our Approach

BxAT

BxA

x

T

xTP

Flow sizeTransfer Time

Solve For A and B

Predict Throughput For Some Other Transfer

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Model Fit is Excellent

Correlation SetLow and Normally Distributed Relative ErrorsAt All Flow Sizes

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Stability

• How long does the TCP throughput function remain stable? – How frequently should we probe the path?

• What’s the distribution of throughput around the function (i.e., the error)?

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Throughput is Stable For Long Periods

Correlation Set

Increasing Max/Min Throughput in Interval

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Throughput Is Normally Distributed In An Interval

Distribution Set

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Online DualPats Algorithm

• Fetch probe sequence for destination– Start probing process if no data exists

• Project probe sequence ahead– 20 point moving average over values with

current sampling interval

• Apply model using projected data

• Return result– confidence interval computed using

normality assumptions

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Dynamic Sampling Rate

• Adjust sampling interval to correspond to the path’s stable intervals

• Limit rate (20 to 1200 seconds)

• Additive increase / additive decrease of based on difference between last two probes

< 5% => increase interval

> 15% => decrease interval

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Finding Sufficiently Large Probe Size

• Default values: 400 KB / 800 KB

• Upper bound

• Additive increase until prediction error are less than threshold, all with same sign.

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Evaluation

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Mean relative error

Mean abs(relative error)

Relative error

P[m

ean

erro

r <

X

]

• Slight conservative bias• >90 % of predictions have < 35% error

Online Evaluation Set

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Conclusions

• Algorithm for predicting the TCP throughput as function of flow size

• Minimal active probing• Dynamic probe rate adjustment

• Explaining flow size / throughput correlation

• Explaining why simple active probing fails

Large scale empirical study

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For MoreInfo

• Prescience Lab– http://plab.cs.northwestern.edu

• Aqua Lab– http://aqualab.cs.northwestern.edu

• D. Lu, Y. Qiao, P. Dinda, and F. Bustamante, Modeling and Taming Parallel TCP on the Wide Area Network, IPDPS 2005 .

• Y. Qiao, J. Skicewicz, P. Dinda, An Empirical Study of the Multiscale Predictability of Network Traffic, HPDC 2004.